MDDI 演讲稿 · 2024-09-30
陈杰豪高级政务部长在医学 AI 中心启动仪式上的开幕致辞
Opening Address by SMS Tan Kiat How at Launch of Centre of AI in Medicine
要点
- • C-AIM（Centre of AI in Medicine）由「全国医疗集团」（NHG）与南洋理工大学（NTU）联合设立——把医疗与 AI 串起来。
- • AI 已经在改写新加坡的医疗：AlphaFold 让人类几年内预测 6 亿+ 蛋白结构（过去半世纪才解了几十万）；AI Singapore 的 JARVIS-DHL 用 AI 识别糖尿病、高血压、高胆固醇风险患者；NUHS 自训 RUSSELL-GPT 写病程总结与转诊信，Endeavour AI 平台预测床位。
- • 「信任」是新加坡的「最大 alpha 源」——医疗行业更不能丢。技术与伦理复杂度（诊断准确性、透明度、训练集多样性、敏感数据安全）必须并行处理。
- • C-AIM 的项目示例：PRIME-CXR 快速分诊胸片、与 Resaro 合作做「评估 AI 临床价值」的稳健框架。
- • C-AIM 同日与 NHG、奥林巴斯（Olympus）、耶鲁医学院（Yale School of Medicine）签署 MOU。
完整译文（中文）
MDDI 英文原文译文 · 翻译日期：2026-05-03
本文已从早期版本的网站迁移过来——格式可能有不一致之处。
数码发展及新闻部高级政务部长 Tan Kiat How 在「医学 AI 中心」（C-AIM）启动仪式上的开幕致辞（2024 年 9 月 30 日）
新加坡李光前医学院（LKC Medicine）治理委员会主席 Lim Chuan Poh 先生；
新加坡全国医疗集团（NHG）集团首席执行官 Joe Sim 教授；
LKC Medicine 院长、医学 AI 中心联合主任 Joseph Sung 教授；
南洋理工大学（NTU）计算与数据科学学院高级副院长、医学 AI 中心联合主任 Miao Chun Yan 教授；
各位女士、先生、嘉宾：
1. 我很高兴出席今天「医学 AI 中心」（Centre of AI in Medicine, C-AIM）的启动仪式——它由全国医疗集团（NHG）与南洋理工大学（NTU）共同设立。
2. 该中心的成立——是新加坡「借力 AI 改善公民健康与福祉」旅程上的又一重要里程碑。
3. 新加坡一直拥抱「以技术克服约束」——同时对权衡保持清醒——并主动地缩小下行影响。这种务实姿态——塑造了我们对几代通用目的技术（从计算机、到互联网、再到其他数字创新）的使用。
4. 我们对 AI 也采取相同的姿态与方法——AI 近期取得了快速进展——尤其在生成式 AI 领域。这一信念——支撑着我们的《国家 AI 战略 2.0》。
5. 我们对 AI 讨论的近期转变感到鼓舞——它已超越最初的炒作——走向更冷静地聚焦「影响」与「投资回报」。对影响的聚焦——增加了我们「借力 AI 解决真实世界问题、为企业与社会带来差别」的能力。对投资回报的强调——也鼓励了更可行的商业模式。没有对这些因素的认真聚焦——很难围绕 AI 构建可持续且有活力的生态。
6. 因此——我很高兴看到——医疗生态中的利益相关方——把心思放在「借力 AI 给我们的社区与患者带来深远影响」上。
7. AI 在医疗中的应用前景广阔——从发现新疗法到改善治疗效果。比如——我们花了半世纪去理解几十万种蛋白的结构。借助 AlphaFold 等 AI 工具——科学家们在短短几年里就预测了 6 亿多种蛋白的结构与相互作用——这彻底改变了药物发现。我们对这一领域的潜力——还只是触及表层。
8. 在更下游——AI 也在应对「劳动力短缺与医疗成本上升」——这些挑战在全球老龄化背景下持续累积。新加坡尤其切身感受到这些约束。预测性诊断等能力——帮助医疗专业人士克服这些挑战、更好地服务患者需求。比如——AI Singapore 的「JARVIS-DHL」项目——用 AI 识别糖尿病、高血压、高胆固醇风险患者——便于早期干预——延缓疾病进展与并发症。
9. AI 也在改造临床工作流与患者旅程。新加坡国立大学医学中心（NUHS）训练了自家的大语言模型「RUSSELL-GPT」——能在几秒内总结患者笔记、撰写转诊信。其「Endeavour AI」平台——还能预测医院床位的可用性——优化容量与患者安置。这些努力赋能医生——更快、更易做出数据驱动的决策——让他们把更多时间放在患者照护上。
10. 当 AI 改造医疗时——「守护信任」至关重要。新加坡的品牌之所以有溢价——是因为我们在所做的一切上获得的信任——无论国内还是国际。这正是黄循财总理称为新加坡「最大 alpha 源」的——我们对「信任、诚信、可靠」的声誉。
11. 同样——「信任」也是医疗的核心——支撑着从药物研发到临床实务的医疗与健康照护的交付。即便我们借力 AI 实现医疗中的承诺——我们必须把这一追求与「守护信任」的努力同步进行。
12. 这意味着应对使用 AI 的「技术与伦理复杂度」——比如确保诊断准确性、透明度——并在多样化数据集上训练 AI 系统、确保跨人群的恰当代表性。我们也必须管理敏感医疗数据的安全与保护。
13. 我很高兴——医学 AI 中心正在推进下一波合作——把 AI 研究转化为「负责任的临床实务」。展位上展示的项目——突出了 C-AIM 在「构建可临床落地的方案」上的努力。
14. 比如「PRIME-CXR」项目——能快速准确地为胸片做分诊——把异常发现优先排队——以加快临床决策。PRIME-CXR 也与初创 Resaro 合作——开发一个稳健框架——评估「哪些 AI 方案带来最高临床价值」。
15. 这些方案之所以可能——是因为 C-AIM 中——AI 研究者、工程师与医疗专家形成了关键纽带——并应用「实施科学」（Implementation Science）方法——以最大化 AI 在医疗中的影响。
16. 这种协作努力——以确保「伦理部署」的基本步骤为基础——从「监督数据访问」的委员会，到关于「知情 AI 实践」的培训与教育，再到与专家及公众围绕「风险感知与有效治理」的定期互动。
17. 展望未来——医疗不仅是国家级挑战，更是全球性挑战——需要跨学科、跨领域、跨行业、跨边界地共享知识与专长。学界、产业、公共部门之间的协作——对促成医疗的「整体性转型」越来越重要。
18. 因此——我很高兴见证 C-AIM 与「全国医疗集团」（NHG）、奥林巴斯（Olympus）、耶鲁大学医学院（Yale School of Medicine）签署的 MOU。
19. 我以感谢所有合作伙伴收尾——感谢在场的研究者、博士生、实务者、社区专家——感谢你们成为这一重要旅程的一部分——共同借力 AI 改造医疗、健康与公共服务的交付。
20. 我期待该中心未来令人激动的项目管线。
21. 非常感谢。
英文原文
MDDI 官网原始记录 · 抓取日期：2026-05-02
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OPENING ADDRESS BY SENIOR MINISTER OF STATE, MINISTRY OF DIGITAL DEVELOPMENT AND INFORMATION, MR TAN KIAT HOW AT THE LAUNCH OF CENTRE AI IN MEDICINE (C-AIM) ON 30 SEP 2024
Mr Lim Chuan Poh, Chairman of the Governing Board at LKC Medicine;
Professor Joe Sim, Group CEO of the National Healthcare Group;
Professor Joseph Sung, Dean of LKC Medicine and Co-Director of the Centre of AI in Medicine;
Professor Miao Chun Yan, Senior Deputy Dean of the NTU College of Computing and Data Science, and Co-Director of the Centre of AI in Medicine;
Ladies and gentlemen, and distinguished guests.
1. It is my pleasure to join all of you today at the launch of the Centre of AI in Medicine, or ‘C- AIM’ in short. The Centre is jointly established by the National Healthcare Group and Nanyang Technological University.
2. The establishment of this Centre is another significant milestone in Singapore's journey to harness AI to improve the health and well-being of our citizens.
3. Singapore has always embraced the use of technologies to overcome our constraints, while remaining clear-eyed about the trade-offs, and actively working to minimise downsides. This pragmatic attitude has shaped our use of generations of general-purpose technologies, from computers, to the internet, to other digital innovations.
4. We adopt the same attitude and approach to AI, which has seen rapid advancements in recent times – especially in the field of generative AI. This belief underpins our National AI Strategy 2.0.
5. We are heartened by the recent shifts in discussions around AI, which have moved beyond the initial hype, towards a more sober focus on impact and returns on investment. The growing focus on impact has increased our ability to harness AI to solve real-world problems and make a difference to businesses and society. The emphasis on returns on investment has also encouraged more viable business models. Without a serious focus on these factors, it is difficult to build a sustainable and vibrant ecosystem around AI.
6. Hence, I am glad to see how stakeholders in the healthcare ecosystem are applying their minds to harness AI, to deliver profound impact to our community and patients.
7. There is significant promise in the use of AI in healthcare – from discovering new therapeutics to improving treatment efficacy. For example, it took us half a century to understand the structures of a few hundred thousand proteins. With AI tools like AlphaFold, scientists have predicted the structures and interactions of over 600 million proteins in just a few years. This has revolutionised drug discovery. And we are just scratching at the surface of the potential of applying AI in this area.
8. Further downstream, AI is also tackling workforce shortages and rising healthcare costs – challenges that continue to mount with an ageing population across the world. Singapore feels these constraints especially acutely. Capabilities like predictive diagnostics help healthcare professionals overcome these challenges and better serve patient needs. For example, AI Singapore’s JARVIS-DHL project uses AI to identify patients at risk of diabetes, hypertension, and high cholesterol for early intervention, thereby slowing disease progression and complications.
9. AI is transforming clinical workflows and the patient journey, too. NUHS has trained its own large language model, RUSSELL-GPT, to summarise patient notes and write referral letters in just seconds. Its Endeavour AI platform also forecasts bed availability in hospitals, better optimising capacity and patient placement. Such efforts empower doctors to make data-driven decisions with speed and ease – allowing them to spend more time on patient care.
10. As AI transforms healthcare, safeguarding trust is of crucial importance. Singapore’s brand has a premium because of the trust we command in whatever we do – both domestically and internationally. This is after all what Prime Minister Lawrence Wong calls Singapore’s ‘greatest source of alpha’ – our reputation for trust, integrity, and reliability.
11. Likewise, trust is also at the heart of healthcare, underpinning the delivery of health and medical care from drug research and development to clinical practices. Even as we harness AI to realise its promise in healthcare, we must twin this pursuit with efforts to safeguard trust.
12. This entails tackling the technical and ethical complexities with using AI, such as ensuring diagnostic accuracy, transparency, and training AI systems on diverse datasets to ensure proper representation across populations. We must also manage the security and protection of sensitive healthcare data.
13. I am glad that the Centre of AI in Medicine is embarking on the next bound of partnerships to translate AI research into responsible clinical practice. Projects showcased at the booths highlight C-AIM’s efforts in building clinically implementable solutions.
14. The PRIME-CXR project for example rapidly and accurately triages chest x-rays to prioritise abnormal findings for faster clinical decisions. PRIME-CXR is also collaborating with start-up, Resaro, to develop a robust framework that evaluates which AI solutions deliver the highest clinical value.
15. These solutions are possible because of the critical nexus of AI researchers, engineers, and healthcare experts in C-AIM, applying Implementation Science methods to maximise the impact of AI in healthcare.
16. This collaborative effort is underpinned by fundamental steps to ensure ethical deployment– from committees that oversee data access, to training and education on informed AI practices, to regular engagements with experts and public on risk perceptions and effective governance.
17. Looking ahead, healthcare will remain not only a national challenge, but a global one – requiring the sharing of knowledge and expertise not just across disciplines and domains, but across sectors and borders too. Collaborations between academia, industry and the public sector will be increasingly important to enable a holistic transformation of healthcare.
18. I am therefore delighted to witness the signing of MOUs between C-AIM and the National Healthcare Group, Olympus, and the University of Yale School of Medicine.
19. I would like to end by thanking all of our partners, all of you here, researchers, PhD students, practitioners and community experts for being part of this important journey as we use AI to transform healthcare, wellness and delivery of such services to the public.
20. I look forward to the Centre’s exciting pipeline of projects moving forward.
21. Thank you so much.